Sold by: nannyML
Deployed on AWS
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NannyML's Multicalibrated Confidence Based Performance Estimation Algorithm
Overview
In production ground truth is often delayed or absent. Traditional data drift detection techniques are noisy and do not only alert to changes that impact model performance.
Performance estimation allows you to estimate performance metrics (ROC-AUC, F1, RMSE, etc) without ground truth. MCBPE is an advanced performance estimation algorithm. It gives you a single metric to monitor, optimize and communicate about your models in production.
Highlights
- Estimate the performance of machine learning models in production when targets are absent or delayed.
- NannyML provides Multicalibrated Confidence Based Performance Estimation (MCBPE) for performance estimation of binary and multiclass classification models.
Details
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Features and programs
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AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Free trial
Try this product free for 7 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $14.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $14.00 |
ml.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $9.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $14.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $14.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $14.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $14.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $14.00 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $14.00 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $14.00 |
Dimensions summary
You pay by the hour for each machine (instance) you run, billed per host hour. Pricing splits into three activities: training the algorithm, batch inference, and real-time inference. Within each activity, you choose from the same set of instance types across two families: general-purpose (m4, m5) and compute-optimized (c4, c5). Larger instance sizes carry higher hourly rates. Your total cost scales with how many hours you run, which instance size you select, and whether you train, run batch inference, or run real-time inference.
Top-of-mind questions for buyers
One host hour is one hour that a single instance runs. Each instance you launch meters its own hours separately. If you run two instances for one hour, you are billed two host hours. Charges accrue only while the instance is running.
Both meter per host hour on the same instance types. Batch inference runs predictions on a stored dataset in scheduled jobs, so hours accrue only during the job. Real-time inference keeps an endpoint running to serve live requests, so hours accrue for as long as the endpoint stays active.
Yes. Training, batch inference, and real-time inference are billed as separate activities. Each meters its own host hours on the instance type you select. Your total combines the hours spent training with the hours spent on whichever inference mode you run. Underlying AWS infrastructure fees apply on top.
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Releasing Multicalibrated Confidence Based Performance Estimation (MCBPE) for binary classification problems.
Additional details
Inputs
- Summary
The input should be a CSV file. It should contain the names of the columns in the first row.
The required columns depend on the "parameters" defined during training. For more information read our documentation notebook .
The required number of rows depend on the chunking method defined during training.
- Limitations for input type
- For now, we only support binary classification problems, so the "problem_type" hyperparameter should be "classification_binary".
- Input MIME type
- text/csv
https://github.com/NannyML/sagemaker_docs_mcbpe/blob/main/notebooks/data/bc_reference.csv
https://github.com/NannyML/sagemaker_docs_mcbpe/blob/main/notebooks/data/bc_analysis.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
y_pred_proba | The values are the predicted scores or probabilities for a specific class.
Notice that the column name mapped to this column type on the "parameters" hyperparameter is defined during training, so you can have a different name for it on your CSV file. | Type: Continuous | Yes |
y_pred | The values are the predicted labels.
Notice that the column name mapped to this column type on the "parameters" hyperparameter is defined during training, so you can have a different name for it on your CSV file. | Type: FreeText | Yes |
y_true | This column type contains actual model targets.
Notice that the column name mapped to this column type on the "parameters" hyperparameter is defined during training, so you can have a different name for it on your CSV file. | Default value: N/A
Type: FreeText
Limitations: Variable only needed during training, not inference. If it is provided during inference realized performance will also be calculated. | No |
feature_column_names | The list of column names for the features our model uses. | Type: FreeText
Limitations: The values are the features of your model. These can be categorical or continuous. NannyML identifies this based on their declared pandas data types. | Yes |
Resources
Vendor resources
Support
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If you have any questions, reach out to support@nannyml.com
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